BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Australian Data Science - ECPv6.16.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Australian Data Science
X-ORIGINAL-URL:https://australiandatascience.net
X-WR-CALDESC:Events for Australian Data Science
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Australia/Brisbane
BEGIN:STANDARD
TZOFFSETFROM:+1000
TZOFFSETTO:+1000
TZNAME:AEST
DTSTART:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210930T163000
DTEND;TZID=Australia/Brisbane:20210930T180000
DTSTAMP:20210909T065257Z
CREATED:20210909T065257Z
LAST-MODIFIED:20210909T065257Z
UID:2636-1633019400-1633024800@australiandatascience.net
SUMMARY:EC Bayes Seminar: Francesca Crucinio\, University of Warwick - A particle method for Fredholm Integral Equations of the First Kind
DESCRIPTION:EC Bayes Seminar: Francesca Crucinio\, University of Warwick – A particle method for Fredholm Integral Equations of the First Kind\nWe present a novel method for the solution of Fredholm integral equations of the first kind\, a set of ill-posed inverse problems which model\, among others\, reconstruction of images from distorted noisy observations and indirect density estimation. This novel method is based upon a non-standard sequential Monte Carlo (SMC) algorithm which provides a stochastic discretisation of a smoothed expectation maximisation scheme (EMS) usually implemented under the assumption of piecewise constant solutions. The stochastic discretisation provided by SMC does not assume piecewise constant signals and results in smooth approximate solutions. We analyse the theoretical properties of the EMS iteration\, showing existence of a fixed point\, and of the corresponding SMC algorithms. We compare the novel method with alternatives using a simulation study and present results for realistic systems\, including motion deblurring and reconstruction of cross-section images of the brain from positron emission tomography. \nRegister now
URL:https://australiandatascience.net/event/ec-bayes-seminar-francesca-crucinio-university-of-warwick-a-particle-method-for-fredholm-integral-equations-of-the-first-kind/
CATEGORIES:Event,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210924T120000
DTEND;TZID=Australia/Brisbane:20210924T133000
DTSTAMP:20210917T054829Z
CREATED:20210917T054829Z
LAST-MODIFIED:20210917T054829Z
UID:2691-1632484800-1632490200@australiandatascience.net
SUMMARY:ADSN Centre Spotlight #5
DESCRIPTION:ADSN Centre Spotlight #5\nJoin us for our fifth ADSN Centre Spotlight as we hear from four partners about what’s happening in their organisation when it comes to data science. \n\nDonna Burnett\, School Manager\, La Trobe Business School\, Centre for Data Analytics and Cognition (CDAC)\nProfessor Ravinesh Deo – The University of Southern Queensland (USQ)’s Advanced Data Analytics Research Group\nProfessor Zahid Islam\, Director\, Charles Sturt Data Science Research Unit (DSRU)\nDistinguished Professor Matt Wand\, Group Leader\, UTS Statistics and Data Science Group\n\nPartner and communications contacts have been sent a Zoom calendar invitation\, but if you would like to join us\, please send us an email.
URL:https://australiandatascience.net/event/adsn-centre-spotlight-5/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210921T110000
DTEND;TZID=Australia/Brisbane:20210921T120000
DTSTAMP:20210916T020334Z
CREATED:20210916T020333Z
LAST-MODIFIED:20210916T020334Z
UID:2666-1632222000-1632225600@australiandatascience.net
SUMMARY:Distinguished Visitor Seminar - Assistant Professor Linda Tan
DESCRIPTION:Distinguished Visitor Seminar – Assistant Professor Linda Tan\nThe Centre for Data Science Distinguished Visitor Seminar Series has been created to provide a platform for our esteemed colleagues from across the nation and the globe to share their research\, knowledge and expertise in the field of data science. \nRegister now \nThe next seminar will be presented by Linda S. L. Tan. Linda is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore. Her research interests are in variational approximation methods and improving the accuracy and rate of convergence of Bayesian computational algorithms. In this seminar\, Linda will present: \nEfficient data augmentation techniques for state space models\nWe propose a data augmentation scheme for improving the rate of convergence of the EM algorithm in estimating Gaussian state space models. The scheme considers a linear transformation of the latent states in which two working parameters are introduced for rescaling and recentering. We derive optimal values of the working parameters by minimizing the fraction of missing information and study their large sample properties and dependence on the persistence and signal-to-noise ratio. An alternating expectation-conditional maximization (AECM) algorithm is designed to take advantage of the proposed scheme and shown to be a more attractive alternative to the centered parametrization (CP) or noncentered parametrization (NCP). We extend earlier results to Bayesian Markov chain Monte Carlo (MCMC) algorithms for non-Gaussian state space models\, focusing on the stochastic volatility and stochastic conditional duration models. A block-specific reparametrization (BSR) strategy for multi-block MCMC samplers is proposed which enables the EM data augmentation scheme to be applied to non-Gaussian models via a mixture of normals approximation. Applications on simulated data and benchmark real data sets indicate that the BSR strategy can yield improvements in simulation efficiency compared with the CP or NCP\, and sometimes even over ASIS (which interweaves the CP and NCP). \n*** This will be an online event only. A Zoom link will be emailed to registrants on the day of the event. ***
URL:https://australiandatascience.net/event/distinguished-visitor-seminar-assistant-professor-linda-tan/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210921
DTEND;VALUE=DATE:20210924
DTSTAMP:20210903T045102Z
CREATED:20210903T044703Z
LAST-MODIFIED:20210903T045102Z
UID:2604-1632182400-1632441599@australiandatascience.net
SUMMARY:Data-driven queueing challenges
DESCRIPTION:Data-driven queueing challenges\nThe increasing availability of data in the operation of large computer networks and the management of human service systems creates new problems in queueing theory. To respond\, ACEMS and NETWORKS are co-sponsoring an on-line workshop on `Data-driven queueing challenges’\, in association with The Alan Turing Institute. \nThe workshop brings together researchers from statistics\, stochastic modelling and data science to consider research directions in modelling and controlling queues\, and in dealing with parameter uncertainty\, when there is access to operational data. \nThe program contains 16 speakers and 2 panel discussions. \nRegister now
URL:https://australiandatascience.net/event/data-driven-queueing-challenges/
CATEGORIES:Online workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210917T120000
DTEND;TZID=Australia/Brisbane:20210917T130000
DTSTAMP:20210916T002825Z
CREATED:20210916T002759Z
LAST-MODIFIED:20210916T002825Z
UID:2660-1631880000-1631883600@australiandatascience.net
SUMMARY:QUT Data Science in the News: Monitoring the nation's pulse
DESCRIPTION:Monitoring the nation’s pulse: The what\, who\, how and why of the Census\nIn this Data Science in the News webinar\, we will explore the important role data science plays in the Australian Census. \nRegister now \nModerator:\nProfessor David Lovell – Deputy Director\, QUT Centre for Data Science \nPanellists:\nMr Mark Harding – Program Manager\, 2021 Census Data Operations \nMs Caroline Deans – Director\, 2021 Census Dissemination \nDr Gentry White – Associate Professor in Data Science and Government Statistics Chair \nDr Aiden Price – Research Associate School of Mathematical Sciences QUT\, Project Manager AusEnHealth \n  \nMore about the Panel Session Topics\nWhat’s new in the 2021 Census – the “what” – Mark Harding \nThe 2021 Census design has been guided by its overarching objectives: smooth-running Census\, garners strong support from the community\, and produces high quality data. Mark Harding will talk through what is new about the 2021 Census\, and in particular how the ABS has adopted a user-centred design approach to delivering the Census. This year the ABS has faced the added challenge of running a Census during the pandemic. Mark will describe how the ABS has responded to COVID-19 and the impacts on Census field operations. \nValue of the Census Data – the “who\, how and why” – Caroline Deans \nCensus data is used to inform important decisions about transport\, schools\, health care\, infrastructure and business. While many people are aware of how the Government uses the data\, the Census is also heavily relied on by community groups and small businesses to improve the lives of individuals. Caroline Deans will cover some case study examples on the varying uses of Census data. \nCaroline will also talk through what happens to the information collected\, from when the data is collected through to when it is transformed into meaningful statistics. The 2021 Census is being conducted at a most interesting time and the data from this Census will be very important to show how the pandemic is affecting our economy and society. \nPartners in Data Science – Dr Gentry White \nDr. White will speak briefly on the unique partnership between QUT\, the CDS and the ABS outlining their current program of research and plans for the future. \nAusEnHealth Project: Climate and Air Quality Vulnerability Index Development – Dr Aiden Price \nThe changing nature of many hazards\, coupled with growing and ageing populations and infrastructure in exposed areas is leading to increased vulnerability across Australia and internationally. AusEnHealth is a multi-agency funded project with the aim to provide tools to support the assessment of population vulnerability through an environmental health lens. This has been achieved by combining air quality and climate data with demographics data\, the latter being comprised almost entirely of Census data from the Australian Bureau of Statistics. \n  \nMore about the Moderator and Panellists\nMr Mark Harding’s career at the ABS spans over two decades\, commencing in 2000. During this time\, Mr Harding has been involved in a number of Censuses and also led the ABS Population Survey Operations. He is currently the Program Manager for 2021 Census Data Operations and is responsible for the end-to-end processes from after the data is collected through to when the data is released. \nMr Harding is based in Sydney where he is currently working from home in his third month of lockdown with his wife and two children. \nMs Caroline Deans commenced her career at the ABS in 2005\, although has been interested in statistics and their importance in decision making well before then. Ms Deans has a wealth of knowledge about the Census\, having worked on the last three Censuses. She was responsible for managing the South Australia Census count in 2011\, the Queensland count in 2016\, and is now responsible for releasing the data for the 2021 Census. \nMs Deans grew up in Adelaide and moved to Brisbane in 2016 where she currently resides. \nDr. Gentry White is the current Associate Professor in Data Science and the Australia Bureau of Statistics Co-Chair in the QUT Centre for Data Science. Dr. White has been at QUT since 2013 and prior was a Research Fellow at the ARC Center for Excellence in Policing and Security and the Institute for Social Science Research at The University of Queensland since 2009. \nDr Aiden Price is a research associate in the Centre for Data Science\, working as a project manager on the AusEnHealth Project: a national environmental health strategic planning digital twin. Aiden’s research is currently focused on spatial and temporal analyses of environmental and population health data\, identifying the impact of bushfires on human health\, and conservation-focused work through the lens of aesthetics in the Antarctic Peninsula. \nProfessor David Lovell is a Professor in the QUT School of Computer Science\, Deputy Director of QUT’s Centre for Data Science\, and leader of the Centre’s Data-Focused Decision-Making Program. David’s research interests lie at the intersection of humanity\, science and technology\, particularly data science. We humans are the ones who stand to benefit (or suffer) from systems that use data to make or inform decisions that affect our lives. David wants to ensure that science and technology are developed\, designed and delivered with this in mind so that our world is better as a result.
URL:https://australiandatascience.net/event/qut-data-science-in-the-news-monitoring-the-nations-pulse/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210915T120000
DTEND;TZID=Australia/Brisbane:20210915T130000
DTSTAMP:20210903T051044Z
CREATED:20210902T231200Z
LAST-MODIFIED:20210903T051044Z
UID:2600-1631707200-1631710800@australiandatascience.net
SUMMARY:ACEMS Virtual Public Lecture - Hypocrisy ++
DESCRIPTION:ACEMS Virtual Public Lecture – Hypocrisy ++\nThere are two popular\, but competing\, philosophical theories which attempt to answer this question\, usually referred to as the ‘subjective’ and the ‘frequency’ approaches. These theories are often claimed to form the foundations\, respectively\, of the Bayesian and frequentist interpretations of statistics. In this public lecture\, Professor Burdzy will explain why this is not actually the case\, and will outline logical contradictions with both these philosophical theories. \nRegister now
URL:https://australiandatascience.net/event/acems-virtual-public-lecture-hypocrisy/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210910T130000
DTEND;TZID=Australia/Brisbane:20210910T150000
DTSTAMP:20210909T004715Z
CREATED:20210902T230619Z
LAST-MODIFIED:20210909T004715Z
UID:2598-1631278800-1631286000@australiandatascience.net
SUMMARY:ADSN Workshop: Synthetic Data
DESCRIPTION:ADSN Workshop: Synthetic Data\nWe’ve had recent interest from industry about simulating realistic data from complex systems. There are many situations where sensitivities of real data make synthetic data safer to handle. We (QUT) would like to convene a workshop to engage researchers with capabilities and interests in synthetic data and open up possibilities for further connection and collaboration. \nPlease contact us if you would like to receive an invitation to this online workshop \n\n\n\n\n\n\n1:00PM – 1:10PM\nWelcome & overview of workshop\nPurpose of workshop and quick introductions\nDistinguish Professor Kerrie Mengersen \nQUT Centre for Data Science\n\n\n1:10PM – 1:20PM\nOverview of synthetic data generation \nIn this overview\, Connor will provide a short literature review and summary of main features when it comes to synthetic data generation.\nConor Hassan\nQUT Centre for Data Science\n\n\n1:20PM – 1:35PM\nGRATIS: GeneRAting TIme Series with diverse and controllable characteristics \nDescription: Synthetic time series are useful for benchmarking and testing methods for forecasting\, clustering\, classification and other tasks. I will discuss an approach to this where we can generate time series with diverse and controllable characteristics using mixture autoregressive (MAR) models. This can be done with the gratis package for R.\nProfessor Rob Hyndman\nMonash University \n\n\n1:35PM – 1:50PM\nDeep Learning Techniques for Dealing with Lack of Data\nIn this talk\, we will present progressive transfer learning methods to deal the data problems such as the lack of labelled data and data shifts. We present an alternative approach to complement the datasets available.\nAssociate Professor Richi Nayak\nQUT Centre for Data Science\n\n\n\n Opportunity for Spotlight Talks \n\n\n\n2:00PM – 2:15PM\nSynthetic data generation using moment-based density estimation \nIn this talk\, we introduce a new synthetic data generation method based on estimated multivariate density function\, which is constructed from the sample moments information of the original data.\nBradley Wakefield\nUniversity of Wollongong (NIASRA) \n\n\n2:15PM – 2:30 PM\nGenerating artificial video data to train machine learning algorithms\nThis talk will present early work on the creation of synthetic video data using motion capture and CGI for use in training of human action recognition models.\nAnthony Paproki\nQUT Centre for Data Science\n\n\n\n Opportunity for discussion\nLearnings & where to from here?
URL:https://australiandatascience.net/event/adsn-workshop-synthetic-data/
CATEGORIES:Online workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210908T100000
DTEND;TZID=Australia/Brisbane:20210908T104500
DTSTAMP:20211012T024454Z
CREATED:20211012T024406Z
LAST-MODIFIED:20211012T024454Z
UID:2722-1631095200-1631097900@australiandatascience.net
SUMMARY:AI4Pandemics Talk #5: Peter Frazier\, Cornell University
DESCRIPTION:Title: Fighting COVID-19 at Cornell University \nYouTube Recording \n\n\nAbstract\nUniversities around the world faced a challenging decision during the summer of 2020: whether to reopen for in-person instruction despite the pandemic and how to protect campus populations if they did. Operations research and data science were a fundamental part of these decisions at Cornell University in the USA. First\, models developed by Cornell’s COVID-19 Mathematical Modeling Team were used to design the testing interventions that are a cornerstone of Cornell’s COVID-19 control strategy: targeted asymptomatic screening that tests all undergraduates twice per week and an adaptive testing program that goes beyond traditional contact tracing to test the full social circle of positive cases. Second\, these same models were the basis for Cornell’s decision to reopen for a fall semester with in-person instruction. They showed that reopening with aggressive mandatory testing was surprisingly less risky than virtual instruction. Data suggested that thousands of students would return to the area whether in-person instruction was offered or not\, and a weaker ability to enforce mandatory testing for these students risked being unable to control clusters in that population. Reopening with asymptomatic screening was successful\, with only 0.5% of students\, staff and faculty infected over the semester. This talk will share insights from this experience and explain practical tools that supported this work.
URL:https://australiandatascience.net/event/ai4pandemics-talk-5-peter-frazier-cornell-university/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210901T120000
DTEND;TZID=Australia/Brisbane:20210901T130000
DTSTAMP:20210812T223224Z
CREATED:20210812T221534Z
LAST-MODIFIED:20210812T223224Z
UID:2400-1630497600-1630501200@australiandatascience.net
SUMMARY:ACEMS Virtual Public Lecture - The Origami of Data Science
DESCRIPTION:ACEMS Virtual Public Lecture – The Origami of Data Science\nMany of us know about origami – where a flat square of paper is folded into a sculpture that inspires appreciation and imagination. In the same way\, we might think about origami (or perhaps more accurately ‘oridēta’) in the context of data science\, whereby a data analysis method or computational algorithm is folded into a software product that inspires interpretation and implementation.\nIn this public lecture\, Professor Mengersen will discuss our attempts at the origami of data science. These include folding new methods and computational approaches into products such as an online atlas of cancer (atlas.cancer.org.au)\, a virtual Great Barrier Reef (virtualreef.org.au)\, an ethical social discourse platform (betterbeliefs.com.au)\, and a personalised learning program (qutschoolofmaths.shinyapps.io/uncoursetoolapp/). \nRegister now \nAlthough the foundations are statistical\, our sculptures require a broad team of experts from the mathematical\, statistical\, and computer sciences\, and they need to be appreciated\, interpreted\, imagined\, and implemented by domain experts and users. \nThis lecture is 45-minute presentation followed by Q & A. \nThis lecture is part of the ACEMS Virtual Public Lecture Series – click here to view other lectures in the series. \nAbout the speaker\nKerrie Mengersen is a Distinguished Professor of Statistics at QUT\, a Deputy Director of ACEMS\, and a Director of the QUT Centre for Data Science. \nKerrie is an elected Fellow of the Australian Academy of Science and the Academy of Social Sciences\, as well as the Queensland Academy of the Arts and Sciences. Her research focuses on Bayesian models and computational methods\, and their application to challenging problems in health\, the environment\, and industry.
URL:https://australiandatascience.net/event/acems-virtual-public-lecture-the-origami-of-data-science/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210825T100000
DTEND;TZID=Australia/Brisbane:20210825T104500
DTSTAMP:20211026T024232Z
CREATED:20210817T013237Z
LAST-MODIFIED:20211026T024232Z
UID:2434-1629885600-1629888300@australiandatascience.net
SUMMARY:AI4Pandemics Talk #4: Joel Miller\, La Trobe University
DESCRIPTION:AI4Pandemics Seminar Series \nSpeaker: Joel Miller La Trobe University \nYouTube Recording \nTitle: COVID and the misunderstood denominator… \nAbstract: Like past epidemics\, the efforts to stop the transmission of SARS-CoV-2 have been hindered by the parallel transmission of misinformation (inaccurate information) as well as disinformation (intentionally deceptive inaccurate information).  Unlike historical epidemics\, the social media landscape has accelerated the spread of misinformation.  I will discuss the role misinformation has played in the pandemic.
URL:https://australiandatascience.net/event/ai4pandemics-talk4-joel-miller-la-trobe-university/
ORGANIZER;CN="Roxanne Jemison":MAILTO:roxanne.jemison@uq.edu.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210824T080000
DTEND;TZID=Australia/Brisbane:20210824T170000
DTSTAMP:20210824T211948Z
CREATED:20210824T045751Z
LAST-MODIFIED:20210824T211948Z
UID:2549-1629792000-1629824400@australiandatascience.net
SUMMARY:Threat or opportunity: Will healthcare Artificial Intelligence de-skill clinicians?
DESCRIPTION:Threat or opportunity: Will healthcare Artificial Intelligence de-skill clinicians?\nProgress in Artificial Intelligence (AI) promises to transform the delivery of healthcare\, including in screening and diagnosis. These AI tools promise to improve the accuracy and speed of results for patients\, and to make clinical workflows more efficient and productive. However AI implementation also raises risks\, including clinician deskilling: deterioration of the practical clinical skills\, decision-making capacity\, and diagnostic reasoning of human clinicians. \nRegister now
URL:https://australiandatascience.net/event/threat-or-opportunity-will-healthcare-artificial-intelligence-de-skill-clinicians/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210823T110000
DTEND;TZID=Australia/Brisbane:20210825T133000
DTSTAMP:20210820T053333Z
CREATED:20210820T052924Z
LAST-MODIFIED:20210820T053333Z
UID:2482-1629716400-1629898200@australiandatascience.net
SUMMARY:Shaping the Future of Data Science: Research Spotlight Series
DESCRIPTION:Shaping the Future of Data Science: Research Spotlight Series\nWelcome to this Research Spotlight Series on ‘Shaping the Future of Data Science’. This conference aims to highlight the excellent research being undertaken by Australia’s female data scientists. \nSpeakers from across the country will give ten-minute spotlight presentations about their work\, challenges and opportunities. We are excited to learn\, share and grow our community in this way. \nFind out more
URL:https://australiandatascience.net/event/shaping-the-future-of-data-science-research-spotlight-series/
CATEGORIES:Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210818T100000
DTEND;TZID=Australia/Brisbane:20210818T104500
DTSTAMP:20211026T024225Z
CREATED:20210722T013921Z
LAST-MODIFIED:20211026T024225Z
UID:2298-1629280800-1629283500@australiandatascience.net
SUMMARY:AI4Pandemics Talk #3: Jeremy Howard\, fast.ai & University of San Francisco
DESCRIPTION:Speaker: Jeremy Howard \nYouTube Recording \nTitle: How a little-known data scientist convinced the West to wear face masks \n\n\nAbstract:\nThe title of this talk is actually the title of an article in The Telegraph  about my journey in co-founding the Masks4All movement. I share how I found myself becoming the face of Masks4All globally\, and what I learned about making an impact as a data scientist.
URL:https://australiandatascience.net/event/ai4pandemics-talk3-jeremy-howard/
LOCATION:Zoom
ORGANIZER;CN="Hamid Khataee":MAILTO:h.khataee@uq.edu.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210813T120000
DTEND;TZID=Australia/Brisbane:20210813T130000
DTSTAMP:20210812T220309Z
CREATED:20210812T220309Z
LAST-MODIFIED:20210812T220309Z
UID:2396-1628856000-1628859600@australiandatascience.net
SUMMARY:Going for Gold: Data Science and the Olympic Games
DESCRIPTION:Going for Gold: Data Science and the Olympic Games\n\nIn this Data Science in the News webinar\, we will explore the important role data science plays in sports and major events like the Olympics.\n\nRegister now\n\n\n\n\n\n\n\nModerator:\n\nDistinguished Professor Kerrie Mengersen – Director\, QUT Centre for Data Science\n\nPanellists:\n\nDr Lachlan Mitchell – Performance Scientist\, Queensland Academy of Sport\, Department of Tourism\, Innovation and Sport\nDr Paul Wu – Senior Lecturer\, Associate Investigator ARC Centre of Excellence for Mathematical & Statistical Frontiers and Centre for Data Science QUT\nDr Allan Hanh – Leader\, Centre of Excellence for Applied Sport Science Research at Queensland Academy of Sport\nProfessor Chris Drovandi – School of Mathematical Sciences\, Program Lead Program Leader Models and Algorithms Centre for Data Science\, QUT\nDr Char-lee Moyle – Senior Lecturer in Management\, QUT Business School\, Co-Leader Social Systems Domain Centre for Data Science\n\nMore about the Panel Session Topics\nDr Lachlan Mitchell: Data in the pool\nLachlan will provide an overview of the data QAS collects and analyses to help out athletes and coaches take on the world in the pool. \nDr Paul Wu: Finding a winning edge through data science: a swimming relay case study\nAs seen in the recent Tokyo Olympics\, swimming relays are a source of medal opportunities for Australia. With ever-increasing competition\, we show how statistical and machine learning approaches can help quantify factors that affect individual and team performances. We also developed a predictive model of gold and medalling probability given team make-up and strategy to support selectors and coaches. \nDr Allan Hanh: Interpreting the results of the Tokyo Olympics\nIn evaluating the performance of various nations at the Olympic Games\, commentators often refer to the number of medals won relative to national population\, but this is a flawed approach. In Tokyo\, the number of medals won was highly related to the size of national economies. The performance of some nations\, however\, differed from predictions derived from that relationship. Exploration of the reasons for the differences could yield insights to guide the evolving design of Australia’s high-performance sport system. \nProfessor Chris Drovandi: Who really won the Olympics?\nIn this talk\, Chris describes a statistical approach that can be used to adjust medal tallies for various factors such as GDP\, population size and number of athletes.  The method is applied to the Tokyo medal tally and some interesting outcomes are revealed. \nDr Char-lee Moyle: Reconceptualising the planning\, monitoring and evaluation of Mega Events: Possibilities for the Brisbane 2032 Olympic Games\nContemporary mega-events\, like the Olympic Games\, are iconic spectacles that can generate substantial economic activity and media attention for host nations\, as well as facilitate global peace and solidarity. However\, historical evidence of cost-blowouts\, exaggerated benefit claims for host nations\, and community disruption detract from potential benefits and have led to a rising crisis of confidence relating to the economic desirability of hosting the Olympic Games. This presentation will explore the history of mega event evaluation and the opportunities for better appraising the benefits for society. \nMore about the Moderator and Panellists\nDr Lachlan Mitchell is a Performance Scientist at the Queensland Academy of Sport specialising in the physiology of elite swimmers. He is a key member of the support team for a number of members of the Australian Dolphins and has supported coaches and athletes who have competed at the last three Olympic Games. Lachlan’s research has centred around new methods of assessing physiological and performance characteristics in elite swimmers\, methods of mathematically describing the relationship between training and performance and using data more effectively to inform training methods. \nDr Paul Wu is a senior lecturer in the School of Mathematical Sciences and a Chief Investigator in the Centre for Data Science (CDS). He is passionate about developing and applying Bayesian and machine learning methods to tackle complex\, real-world problems. Paul leads a number of collaborative projects between data science researchers\, applied researchers and industry practitioners\, especially in ecology\, and sports and fitness. \nDr Allan Hanh is a Strategic Advisor to the research unit of the Queensland Academy of Sport\, and also holds appointments as an Adjunct Professor at the University of Canberra and Griffith University. He is a former Chief Scientist of the Australian Institute of Sport\, where he worked for 27 years and still has an Honorary Emeritus position. He has a long history of involvement in research and the application of findings to practical work with sports. His research activities have included areas such as talent identification\, preparation of athletes for competition in the heat\, altitude training\, doping detection\, and development of technologies aimed at effective athlete monitoring in laboratory and field situations. \nProfessor Chris Drovandi is a Professor in Statistics and Data Science at the Queensland University of Technology (QUT)\, Australia.  He is currently the lead of the QUT Centre for Data Science Models and Algorithms Program. He is an Associate Investigator of the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers\, and an Associate Editor of Statistics and Computing. \nDr Char-lee Moyle is a Senior Lecturer in the School of Management and the Advance Queensland Innovation Metrics Mid-Career Research Fellow at the Queensland University of Technology. She is the co-lead of the Social Systems domain of the Centre for Data Science and a member of the Centre for Future Enterprise. Her domain expertise lies in the field of tourism and event economics.
URL:https://australiandatascience.net/event/going-for-gold-data-science-and-the-olympic-games/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210729T140000
DTEND;TZID=Australia/Brisbane:20210729T153000
DTSTAMP:20210702T020141Z
CREATED:20210702T020141Z
LAST-MODIFIED:20210702T020141Z
UID:2175-1627567200-1627572600@australiandatascience.net
SUMMARY:Data Science Under the Hood: Manifold Learning
DESCRIPTION:Data Science Under the Hood: Manifold Learning\nThis talk introduces Manifold Learning\, the technique to uncover the intrinsic shape of the original data. We also discuss how different manifold learning paradigms can be designed to be incorporated to a dimensionality reduction technique to learn the accurate low-dimensional data representation. \nRegister now
URL:https://australiandatascience.net/event/data-science-under-the-hood-manifold-learning/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210728T100000
DTEND;TZID=Australia/Brisbane:20210728T104500
DTSTAMP:20211026T024218Z
CREATED:20210715T035313Z
LAST-MODIFIED:20211026T024218Z
UID:2278-1627466400-1627469100@australiandatascience.net
SUMMARY:AI4Pandemics Talk #2: Kirsty Short\, UQ
DESCRIPTION:Speaker: Dr Kirsty Short\, The University of Queensland \nYouTube Recording \nTitle: The role of children in the spread of SARS-CoV-2 \nAbstract: \nThe role of children in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains highly controversial. To address this issue\, we performed a meta-analysis of the published literature on household SARS-CoV-2 transmission clusters (n = 213 from 12 countries). Only 8 (3.8%) transmission clusters were identified as having a pediatric index case. Asymptomatic index cases were associated with a lower secondary attack in contacts than symptomatic index cases (estimate risk ratio [RR]\, 0.17; 95% confidence interval [CI]\, 0.09-0.29). To determine the susceptibility of children to household infections the secondary attack rate in pediatric household contacts was assessed. The secondary attack rate in pediatric household contacts was lower than in adult household contacts (RR\, 0.62; 95% CI\, 0.42-0.91). These data have important implications for the ongoing management of the COVID-19 pandemic\, including potential vaccine prioritization strategies.
URL:https://australiandatascience.net/event/ai4pandemics-talk-2-kirsty-short-uq/
LOCATION:Zoom
ORGANIZER;CN="Hamid Khataee":MAILTO:h.khataee@uq.edu.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210726T110000
DTEND;TZID=Australia/Brisbane:20210802T150000
DTSTAMP:20210610T053238Z
CREATED:20210610T053238Z
LAST-MODIFIED:20210610T053238Z
UID:2025-1627297200-1627916400@australiandatascience.net
SUMMARY:ECSSC2021
DESCRIPTION:Early Career and Student Statistician Conference 2021 (formerly Young Statisticians Conference) \nThe Early Career & Student Statisticians Conference (ECSSC) 2021 will be held from 26 July to 1 August 2021. We are delighted to announce that we will be holding our conference virtually! ECSSC2021 will bring together the best students and early-career professionals in statistics and data analysis from all around Australia. This event is not to be missed! For the first time we are offering a day for high school students with two streams (years 7-10 and 11-12). Registration for high school students is free of charge. \nTo keep up-to-date with ECSSC2021\, please go to the official conference website. \nWe are also offering a range of pre conference workshops that can be viewed here. \n 
URL:https://australiandatascience.net/event/ecssc2021/
ATTACH;FMTTYPE=image/png:https://australiandatascience.net/wp-content/uploads/2021/06/ECSSC-Logo.png
ORGANIZER;CN="Statistical Society of Australia (SSA)":MAILTO:eo@statsoc.org.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210725T110000
DTEND;TZID=Australia/Brisbane:20210725T150000
DTSTAMP:20210708T000706Z
CREATED:20210707T235855Z
LAST-MODIFIED:20210708T000706Z
UID:2188-1627210800-1627225200@australiandatascience.net
SUMMARY:Workshop: Statistical Shape Analysis via Topological Data Analysis
DESCRIPTION:The Statistical Society of Australia and the Early Career and Student Statistician Conference are offering their 2nd short course leading up to the conference\, to be held virtually on \n25 Jul 2021\, 11:00 AM – 3:00 PM AEST. \nAbout the short course: \nAs modern data applications become complex in size and structure\, identifying the underlying shape and structure has become of fundamental importance. The classical approaches such as dimension reduction are challenging for handling these applications. Topological data analysis (TDA) is a rapidly developing collection of methods that focuses on the “shape” of data. TDA can uncover the underlying low-dimensional geometric and topological structures from high-dimensional datasets. TDA has been successfully applied to various areas\, including biology\, network data\, material science\, and geology\, in recent years. The goal of the lecture is to introduce novel TDA methods that can capture geometric or topological information of data and make statistical inferences. This lecture aims to familiarize these new methods along with their applications to various types of data. \nAbout the presenter: \nChul Moon received his Ph.D. in Statistics from the University of Georgia. He joined the Department of Statistical Science at Southern Methodist University as an Assistant Professor in 2018. His research interests include topological data analysis\, empirical likelihood\, and ranked set sampling. His research aims to develop statistical methods in biosciences and geosciences. \nPrerequisites: \nBasic statistical knowledge and R. \nFor more information and to register please click here.
URL:https://australiandatascience.net/event/workshop-statistical-shape-analysis-via-topological-data-analysis/
LOCATION:Zoom
CATEGORIES:Online workshop
ORGANIZER;CN="Statistical Society of Australia (SSA)":MAILTO:eo@statsoc.org.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210724T110000
DTEND;TZID=Australia/Brisbane:20210724T150000
DTSTAMP:20210708T000603Z
CREATED:20210708T000439Z
LAST-MODIFIED:20210708T000603Z
UID:2194-1627124400-1627138800@australiandatascience.net
SUMMARY:Workshop: Convex Optimization for Statistical and Machine Learning with CVXR
DESCRIPTION:The Statistical Society of Australia and he Early Career Student Statistician Conference 2021 are offering this workshop on Convex Optimization for Statistical and Machine Learning with CVXR. \nOptimization plays an important role in fitting many statistical models. Some examples include least squares\, ridge and lasso regression\, Huber regression\, and support vector machines. CVXR is an R package that provides an object-oriented modeling language for convex optimization. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather than the standard form required by most solvers. Moreover\, problems can be easily modified and re-solved\, making the package ideal for prototyping new statistical methods. First\, the user specifies an objective and set of constraints by combining constants\, variables\, and parameters using a library of functions with known mathematical properties. CVXR then applies disciplined convex programming (DCP) to verify the problem’s convexity. Once verified\, the problem is automatically converted into quadratic or conic form and passed to a solver like OSQP\, MOSEK\, or GUROBI. We demonstrate CVXR’s modeling framework with several applications in statistics and machine learning. \nWe will begin with a gentle introduction to convex optimization using examples from ordinary least squares and penalized regression. This will be followed by a high-level description of CVXR\, how it differs from other packages\, and a discussion of the domain specific language that CVXR implements. We will show how CVXR works on different classes of problems\, such as linear programs\, quadratic programs\, and semidefinite programs\, and demonstrate its usage with a variety of examples. Finally\, we will have a segment for potential developers in which we go over the nuts and bolts of adding new functions to CVXR’s library. \nAbout the presenter: Anqi Fu is a Ph.D candidate in the Electrical Engineering department at Stanford University. Her research focuses on developing algorithms and software for large-scale optimization with applications to data science. One of her recent projects leverages methods from optimal control to design treatment plans for cancer radiation therapy. Prior to starting her Ph.D\, Anqi worked as a machine learning scientist at H2O.ai. She received an M.S. in Statistics from Stanford University\, and a B.S. in Electrical Engineering and a B.A. in Economics from the University of Maryland\, College Park. \nPrerequisites:  A working knowledge of statistics and linear algebra\, and basic experience with a scripting language like R. We also invite attendees to bring problems of interest\, which we will do our best to formulate and solve in CVXR. \nFor more information and to register please click here. \n 
URL:https://australiandatascience.net/event/workshop-convex-optimization-for-statistical-and-machine-learning-with-cvxr/
LOCATION:Zoom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210714T173000
DTEND;TZID=Australia/Brisbane:20210714T190000
DTSTAMP:20210617T013848Z
CREATED:20210617T013848Z
LAST-MODIFIED:20210617T013848Z
UID:2046-1626283800-1626289200@australiandatascience.net
SUMMARY:A Celebration of Mathematics – Diversity in STEM
DESCRIPTION:A Celebration of Mathematics – Diversity in STEM\nAMSI welcomes all students\, researchers and professionals with an interest in STEM to join the team for a relaxed evening of talks and lively discussions! \nRegister now
URL:https://australiandatascience.net/event/a-celebration-of-mathematics-diversity-in-stem/
CATEGORIES:Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210714T120000
DTEND;TZID=Australia/Brisbane:20210714T130000
DTSTAMP:20210625T054601Z
CREATED:20210625T053614Z
LAST-MODIFIED:20210625T054601Z
UID:2158-1626264000-1626267600@australiandatascience.net
SUMMARY:ACEMS Virtual Public Lecture - A Song of Wind & Fire
DESCRIPTION:ACEMS Virtual Public Lecture – A Song of Wind & Fire: a statistical journey through an uncertain world\n\n\nIn this lecture\, ACEMS Associate Investigator Dr Rachael Quill will explore how shedding light on the uncertainties of wind flow across the environment can support informed decision-making in bushfire management and renewable energy generation.\nThe weather and its uncertainties influence our decisions every day. Did you take an umbrella today\, just in case\, or did you get caught in that shower? In many scenarios\, being unprepared for the unknown might only mean a dampening of our pride. But in others\, the cost of not understanding uncertainty can be catastrophic. \n\nRegister now
URL:https://australiandatascience.net/event/acems-virtual-public-lecture-a-song-of-wind-fire/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210714T100000
DTEND;TZID=Australia/Brisbane:20210719T104500
DTSTAMP:20211026T024210Z
CREATED:20210709T023716Z
LAST-MODIFIED:20211026T024210Z
UID:2221-1626256800-1626691500@australiandatascience.net
SUMMARY:AI4Pandemics Talk #1: Chris Rackauckas\, MIT
DESCRIPTION:The first AI4PAN seminar speaker will be Chris Rackauckas (MIT) \nYouTube Recording \nTitle: \nLearning Epidemic Models That Extrapolate. \nAbstract: \nModern techniques of machine learning are uncanny in their ability to automatically learn predictive models directly from data. However\, they do not tend to work beyond their original training dataset. Mechanistic models utilize characteristics of the problem to ensure accurate qualitative extrapolation but can lack in predictive power. How can we build techniques which integrate the best of both approaches? In this talk we will discuss the body of work around universal differential equations\, a technique which mixes traditional differential equation modeling with machine learning for accurate extrapolation from small data. We will showcase how incorporating different variations of the technique\, such as Bayesian symbolic regression and optimizing the choice of architectures\, can lead to the recovery of predictive epidemic models in a robust way. The numerical difficulties of learning potentially stiff and chaotic models will highlight how most of the adjoint techniques used throughout machine learning are inappropriate for learning scientific models\, and techniques which mitigate these numerical ills will be demonstrated. We end by showing how these improved stability techniques have been automated and optimized by the software of the SciML organization\, allowing practitioners to quickly scale these techniques to real-world applications. \n\n 
URL:https://australiandatascience.net/event/ai4pan-seminar-series-with-chris-rackauckas-mit/
LOCATION:Zoom
CATEGORIES:Seminar
ORGANIZER;CN="Hamid Khataee":MAILTO:h.khataee@uq.edu.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210712T080000
DTEND;TZID=Australia/Brisbane:20210723T170000
DTSTAMP:20210507T075841Z
CREATED:20210507T075306Z
LAST-MODIFIED:20210507T075841Z
UID:1909-1626076800-1627059600@australiandatascience.net
SUMMARY:2021 AMSI Winter School on Statistical Data Science
DESCRIPTION:2021 AMSI Winter School on Statistical Data Science\nAMSI and Queensland University of Technology are proud to present the 2021 Winter School on Statistical Data Science from 12-23 July. \nFor the first time\, the program will be hosted virtually with options for students to attend event hubs in selected states. Boasting an impressive speaker line-up\, attendees can delve deeper into modules focusing on: \n\nBayesian statistics\,\nAdvanced Markov chains and Monte Carlo methods\nLikelihood-free inference\nModern neural networks\nDimension reduction for high dimensional data\n\nThis event is aimed at postgraduate students\, early career researchers and industry professionals wanting to sharpen their skills. \nApplications are now open and will close at 11.59pm on Sunday 20 June. \nScholarships are also available to AMSI Member students requiring financial assistance to cover program fees. To apply\, go to https://ws.amsi.org.au/apply-for-a-scholarship/ \nFor any further enquiries\, please contact coordinator_rhed@amsi.org.au or visit our website for more details https://ws.amsi.org.au/ \nIf you are an academic and know of someone who may be interested in attending\, we encourage you to forward these details and spread the word about the program. \nApply Now
URL:https://australiandatascience.net/event/2021-amsi-winter-school-on-statistical-data-science/
CATEGORIES:Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210705T090000
DTEND;TZID=Australia/Brisbane:20210709T150000
DTSTAMP:20210610T051526Z
CREATED:20210610T051526Z
LAST-MODIFIED:20210610T051526Z
UID:2017-1625475600-1625842800@australiandatascience.net
SUMMARY:Australian and New Zealand Statistical Conference (ANZSC2021)
DESCRIPTION:Australian and New Zealand Statistical Conference (ANZSC2021) \nThe organising committee warmly invites you to the 2021 Australian and New Zealand Statistical Conference\, which will take place online from the 5th to the 9th July 2021. \nThis conference brings together three leading statistical communities – the Statistical Society of Australia\, the New Zealand Statistical Association – and the Australian Conference on Teaching Statistics. \nThe aim of this Conference is to bring together a broad range of researchers and practitioners across a variety of statistical disciplines to facilitate the exchange of theory\, methods and applications. \n  \n  \nWith these three societies working together there will be strong program components of interest to a wide diversity of academic\, government\, and industry colleagues. This includes the full spectrum of delegates from those advancing theoretical methodology to those working on industry applications (in traditional and non-traditional statistical areas). Of particular interest is how Big Data continues to impact all of us. \nThe program is available here.
URL:https://australiandatascience.net/event/australian-and-new-zealand-statistical-conference-anzsc2021/
LOCATION:Virtual – Zoom and Slack\, Australia
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210629
DTEND;VALUE=DATE:20210701
DTSTAMP:20210624T020310Z
CREATED:20210624T015831Z
LAST-MODIFIED:20210624T020310Z
UID:2098-1624924800-1625097599@australiandatascience.net
SUMMARY:ACEMS Impact Workshop for Researchers and Students
DESCRIPTION:ACEMS Impact Workshop for Researchers and Students\nACEMS is pleased to invite you to attend a workshop on the topic of Research Impact\, tailored for the mathematical sciences.  It will be co-facilitated by Impact Research Academy and ACEMS. \nWorkshop Details – Please RSVP (for Zoom Links) \nAims and Benefits of Workshop \nThis workshop is designed\, firstly\, to be of practical career benefit to participating researchers and research students and\, secondly\, to help you generate outputs to communicate and promote the benefits of your research. \nThe workshop will help participants to: \n\nunderstand the growing significance of research impact in the Australian\, and wider\, research context – and provide practical knowledge to help you in your research career\nthink about the benefits\, within and beyond academia\, realised from the mathematical sciences (from theoretical to applied) and your research\nbuild and share knowledge\, and grow capabilities\, around research impact\, including best practices for creating\, capturing\, valuing\, and communicating your research impact\nengage in practical activities designed to help you\n\nexplore a mixture of methods to value your research impact\ncommunicate some of your research impact (prospectively or retrospectively)\n\n\nenjoy discussions designed to help us all think about how we can amplify our impact\nhelp you prepare research impact statements/narratives\, including for use in reporting and promoting your work\n\nThe workshop will be held online (over Zoom) over two part-days.  Ideally\, participants will attend both days (or as much as they can). \n\nDates: 29th & 30th June\nTime: 9.30am – 12.30pm AEST\nWhere: Via Zoom (note there is a different link for each day)\nPlease click on this Eventbrite page to:\n\nview the program overview for both days\nregister to attend (you will receive Zoom links following registration)\nstay updated on any developments (including local node activities linked to the workshop)
URL:https://australiandatascience.net/event/acems-impact-workshop-for-researchers-and-students-2/
CATEGORIES:Online workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210623T093000
DTEND;TZID=Australia/Brisbane:20210623T103000
DTSTAMP:20210617T012300Z
CREATED:20210617T012259Z
LAST-MODIFIED:20210617T012300Z
UID:2043-1624440600-1624444200@australiandatascience.net
SUMMARY:Bayesian hierarchical stacking—All models are wrong\, but some are somewhere useful
DESCRIPTION:Bayesian hierarchical stacking—All models are wrong\, but some are somewhere useful\nStacking is a widely used model averaging technique. Like many other ensemble methods\, stacking is more effective when model predictive performance is heterogeneous in inputs\, in which case we can further improve the stacked mixture with a hierarchical model. In this talk I will focus on the recent development of Bayesian hierarchical stacking: an approach that locally aggregates models. The weight is  a function of data\, partially-pooled\, inferred using Bayesian inference\,  and can further incorporate other structured priors and complex data. I will also discuss some theory bounds: when and why model averaging is useful; what model dissimilarity metric is relevant to Bayesian ensembles. \nRegister now
URL:https://australiandatascience.net/event/bayesian-hierarchical-stacking-all-models-are-wrong-but-some-are-somewhere-useful/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210622T180000
DTEND;TZID=Australia/Brisbane:20210622T193000
DTSTAMP:20210604T042940Z
CREATED:20210602T231905Z
LAST-MODIFIED:20210604T042940Z
UID:1994-1624384800-1624390200@australiandatascience.net
SUMMARY:AI and the Future of Education
DESCRIPTION:AI and the Future of Education\nMonash Education\, in collaboration with the Monash Data Futures Institute\, is organizing a panel discussion on “AI and the future of education” with internationally acclaimed researchers who will cast a critical eye on the increasing attention being paid to AI-driven applications and systems in education. \nThe panel will explore questions like “what forms of AI technology are being implemented in education\, and what implications do they have for students\, teachers and education institutions?” and “how do the imagined educational benefits of AI contrast with the practical limitations of actually using these technologies?”. \nRegister now
URL:https://australiandatascience.net/event/ai-and-the-future-of-education/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210621T000000
DTEND;TZID=Australia/Brisbane:20210723T000000
DTSTAMP:20210610T054119Z
CREATED:20210610T054008Z
LAST-MODIFIED:20210610T054119Z
UID:2031-1624233600-1626998400@australiandatascience.net
SUMMARY:ECSSC2021 Video Competition
DESCRIPTION:ECSSC2021 Video Competition  \nSubmissions will open shortly for ECSSC2021 Video Competition. Working on any research? Put a video together and demonstrate your ability to concisely disseminate your research to a wider audience. Any student or early career statistician (within 5 years of graduation) in a statistics related field is welcome to enter the competition (sorry – previous winners are excluded). There is no entry fee! \nFor inspiration\, please consider viewing previous submissions and winners. All submitted videos have been posted in the here. \n\n\n\nSubmissions open\n21st June 2021\n\n\nSubmissions close\n23rd July 2021\n\n\nPeople’s choice voting opens\n26th July 2021\n\n\nPeople’s choice voting closes\n1st August 2021\n\n\nWinners Announced\n1st August 2021
URL:https://australiandatascience.net/event/ecssc2021-video-competition/
ORGANIZER;CN="Statistical Society of Australia (SSA)":MAILTO:eo@statsoc.org.au
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Brisbane:20210616T140000
DTEND;TZID=Australia/Brisbane:20210616T150000
DTSTAMP:20210604T072211Z
CREATED:20210604T072151Z
LAST-MODIFIED:20210604T072211Z
UID:2006-1623852000-1623855600@australiandatascience.net
SUMMARY:Hosted by ACEMS | Oracle for Research Grants to Support Researchers\, Data & Projects
DESCRIPTION:Hosted by ACEMS | Oracle for Research Grants to Support Researchers\, Data & Projects\nYou are invited to attend an ACEMS event about free computational grants and community support for researchers and collaborative projects with Oracle for Research. Please see below for more information\, and register via EventBrite. \nACEMS is pleased to host this online talk\, Q&A\, and discussion\, with guests from Oracle for Research. \nOverview: \nIn this online session\, you can learn about opportunities to accelerate research to results\, and gain support for collaborative projects\, using cloud computing and computational grants from Oracle for Research. \nBenefits of Oracle for Research grants will be discussed\, including: \n\nAccess to free cloud credits to support your AI\, HPC and GPU workloads\nAccess to scalable cloud-computing capabilities and advanced analytics for data-intensive research experiments\nOpportunities to connect with other research institutions and industry partners\nReceive technical assistance with Oracle Cloud platform and infrastructure\n\nNote: there are other/further benefits for research students\, entrepreneurs\, and also projects eligible for Oracle’s Community Model Support. \nTopics covered will include: \n\nWhat is Oracle for Research?\nOracle for Research team members supporting researchers and facilitating access to grants and community support\nCase studies for Oracle for Research Grants\nThe easy Oracle for Research grant application process and what to do if you’re interested in applying\nHow Oracle for Research is seeking to support and build community\nAnswers to FAQ About Grants (No\, Oracle does not own your data/IP)\n\nQ&A and Discussion: \nFollowing the presentation from Oracle for Research guests\, there will be a moderated Q&A and discussion. This will provide an opportunity to: \n\nAsk any questions about the Oracle for Research grants and Community Support Model\nEngage in a moderated discussion about research and projects which may be suitable for support from Oracle for Research\nShare ideas for possible new projects (including\, for example\, in relation to environmental modelling\, health\, bioinformatics\, geospatial applications – and more)\n\nWho should attend? \nThis session is designed to be of particular benefit to researchers (across diverse disciplines)\, research students\, research partners\, and others from outside academia who may be interested in collaboration (and grant support). \nAttendees will include those from mathematics\, statistics\, data science\, and other research disciplines\, plus ACEMS extended network and other interested guests\, including from the Australian Data Science Network\, industry and otherwise outside academia. \nFeel free to invite others in your network who may be interested in attending. \nRSVP to Attend: \nPlease RSVP to attend\, via this Eventbrite form. You will then receive the Zoom link to join the online event. \n 
URL:https://australiandatascience.net/event/hosted-by-acems-oracle-for-research-grants-to-support-researchers-data-projects/
CATEGORIES:Online workshop
END:VEVENT
END:VCALENDAR